Glaucoma is a chronic eye condition in which the nerve that connects the eye to the brain (optic nerve) is progressively damaged. Patients with early glaucoma do not have visual symptoms. Progression of the disease results in loss of peripheral vision, so patients may complain of “tunnel vision” (only being able to see the centre). Advanced glaucoma is associated with total blindness.
Worldwide, glaucoma is the second leading cause of blindness, affecting 60 million people by 2010, and responsible for approximately 5.2 million cases of blindness (15% of the total burden of world blindness). The problem is even more significant in Asia, as Asians account for approximately half of the world's glaucoma cases. Finally, because it is a condition of aging, it will affect more people in Singapore and Asia with population aging.
Vision loss in glaucoma cannot be recovered. However, as treatment can prevent progression of the disease, early detection is critical to prevent blindness. Routine screening for glaucoma in the whole population is not cost effective and is limited by poor sensitivity of current tests. However, screening may be useful for high risk individuals, such as first degree relatives of a glaucoma patient, older age (e.g., 65 years and older) and elderly Chinese women (who are at risk of angle closure glaucoma).
Currently, there is no systematic way to detect and manage early glaucoma. Glaucoma patients are often unaware they have the condition, and consult ophthalmologists (eye doctors) only when severe visual loss is already present.
Treatment at this stage is limited to surgery, is expensive, requires skilled personnel, and does not restore vision.
There are three current methods to detect glaucoma:
(1) Assessment of raised intraocular pressure (IOP),
(2) Assessment of abnormal visual field
(3) Assessment of damage to the optic nerve
IOP measurement is neither specific nor sensitive enough to be an effective screening tool and visual field testing requires special equipment only present in tertiary hospitals.
Assessment of damage to the optic nerve is more promising and superior to IOP or visual field testing. However, it presently requires a trained specialist (ophthalmologist), or the use specialized equipment such as the HRT (Heidelberg Retinal Tomography) system. Optic disc assessment by an ophthalmologist is subjective and the availability of HRT is very limited because of the cost involved as well as a shortage of trained operators.
It has been observed that glaucoma is associated with “optic disc cupping” in which the central “cup” portion of the optic disc becomes larger in relation to the disc. Optic disc cupping is accordingly one of the most important features in the diagnosis of glaucoma. For the optic disc, a variational level-set approach, which is based on global optimization concepts, can be used to segment the disc boundary and extract the optic disc region from the retinal image. However, segmentation of the optic cup is more challenging than the optic disc due to the optic cup's interweavement with blood vessels and surrounding tissues. Once the cup has been found a cup-to-disc ratio (CDR) value can be obtained. The CDR values are used for the glaucoma screening, diagnosis and analysis.
Segmentation and learning are two classes of widely used methods for optic cup localisation, and several techniques are known for automated glaucoma diagnosis.
For example, the ARGALI (an Automatic cup-to-disc Ratio measurement system for Glaucoma AnaLlysIs) system has previously been developed for glaucoma detection based on a single non-stereo fundus image (PCT/SG2008/000186). In ARGALI, the cup-to-disc ratio is used to automatically measure the optic nerve. The ARGALI system makes use of contour-based methods in the determination of the cup and disc from retinal images, through analysis of pixel gradient intensity values throughout the retinal image. On some occasions, where the gradient values are gradual, difficulties in the correct cup identification can occur.
Another known system, known as the AGLAIA system (disclosed in PCT/SG2010/000434), uses up to 13 image cues for glaucoma diagnosis/screening. However, it has a limitation of confusing of images with very small cups as large cups. Moreover, errors from optic disc and cup segmentation affect the extraction of other features.
In another “kink-based” system that was previously reported, analysis of blood vessel architecture was used to determine the location of the cup within the optic disc. Using this method, bends in the retinal vasculature over the cup/disc boundary, also known as kinks, were used to determine the physical location of the optic cup. Although this method is not reliant on color or pallor, some of the challenges include correct identification of kinks, as well as the non-presence of kinks in some retinal images.
Some, other work has also been presented making use of information from stereo photographs for the determination of the optic cup and disc. While some of the results presented are promising, the key challenge lies in the use of stereoscopic photography as compared to monocular photography. Stereoscopic photography demands specific hardware and requires specialized training, both of which may be unsuitable for the needs for mass screening.
Discriminatory color-based analysis has also been used to determine the location of the cup and disc from retinal images. Histogram color analysis was performed on the image to determine the threshold cutoff between the cup and the disc. To determine the disc, statistical analysis of the pixel intensities was performed on different features of the retinal image. However, no results were presented on the accuracy of results compared to clinical ground truth.
Furthermore, a sliding window-based regional ranking approach has been proposed for optic cup localization. Sliding windows are used to generate as many cup candidates as possible, and a pre-learned regression model is used to rank all the candidates. Finally an unique cup location is determined using non-maximal suppression (NMS). This method is relatively slow, and in fact presently takes 6 mins per image.
In other previous work, the input disc image is first segmented into “superpixels”. Superpixels corresponding to blood vessels are next removed. The remaining superpixels are labelled as the cup or rim based on structure priors and the labels are propagated to remaining superpixels. After the superpixel label refinement, a cup location is determined by ellipse fitting. This method is the state-of-the-art optic cup localization method, which has high accuracy and relatively high speed. However, a yet higher speed and accuracy would be desirable.
In summary, segmentation methods have been used widely for cup localization, segmentation methods in the past decade. Recently, some learning based method has been introduced and achieved some improvement. However, most of these methods aim at either small CDR error or diagnosis accuracy, and the practical requirements are difficult to meet by current state-of-art methods.